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analysis tools (e.g., NetworkX or equivalent) Familiarity with permutation-based statistical methods (MR-QAP preferred) Interest in human-computer interaction Special Instructions to Applicants Budget
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Posting Details Student Title Classification Information Quick Link https://chapman.peopleadmin.com/postings/39195 Job Number SE181324 Position Information Department or Unit Name Fowler School
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intelligence models for the analysis of multispectral remote sensing imagery. The main tasks include implementing computer vision and machine learning methods for the detection and prediction of algal blooms in
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other areas (financial counseling, precertification and registration). Ability to work with multiple different computer systems with different rules and operational methods essential. Must be able
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
estimation and continuous tracking of tumbling, non-cooperative satellites. This grant focuses on novel methods for Fault Detection, Isolation, and Recovery (FDIR) for autonomous spacecraft, exploring
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high school education in lieu of required experience) Required License No If yes, what is the required licensure/certification? Required Computer Applications: Microsoft Excel, Microsoft Word, Microsoft
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23 Feb 2026 Job Information Organisation/Company Instituto Politécnico de Setúbal Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions PhD
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measurements at 18-digit accuracy using an optical clock network. Nature 591, 564–569 (2021). https://doi.org/10.1038/s41586-021-03253-4 [2] Chave, A. D. (2019). A multitaper spectral estimator for time-series
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-driven methods provide excellent performance under low or cyclo-stationary regimes but struggle with highly dynamic and rapidly varying conditions; conversely, model-based state observers ensure robustness
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, or demonstrable projects in computer vision, multi-modal ML, digital twins or biomedical ML. Familiarity with uncertainty quantification and model explainability methods. Strong software engineering practices: Git